The LMIPCA program: A graphical user interface for fitting the linked-mode PARAFAC-PCA model to coupled real-valued data

Behavior Research Methods

Volumn 41, Issue 4, 2009, Pages 1073-1082

  Wilderjans, Tom F ^a   Ceulemans, Eva ^a   Kiers, Henk A L ^b   Meers, Kristof ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; COMPUTER INTERFACE; COMPUTER PROGRAM; HUMAN; PRINCIPAL COMPONENT ANALYSIS; REVIEW; STANDARD; STATISTICAL MODEL;

ALGORITHMS; HUMANS; MODELS, STATISTICAL; PRINCIPAL COMPONENT ANALYSIS; REFERENCE STANDARDS; SOFTWARE; USER-COMPUTER INTERFACE;

EID: 74949104277     PISSN: 1554351X     EISSN: None     Source Type: Journal    
DOI: 10.3758/BRM.41.4.1073     Document Type: Article

References (40)

1. BEIX, R. W. (1974). An exploratoiy assessment of situational effects in buyer behavior. Journal of Marketing Research, 11, 156-163.
2. BRO, R. (1997). PARAFAC. Tutorial and applications. Chemometrics & Intelligent Laboratory Systems, 38, 149-171. doi:10.1016IS0169-7439(97)00032-4
3. BRO, R. (1998). Multi-way analysis in the food industry: Models, algorithms. and applications. Unpublished doctoral dissertation, University of Amsterdam.
4. BRO, R., ANDEESSON, C. A., & KIERS, H. A. L. (1999). PARAFAC2: Part II. Modeling chromatographic data with retention time shifts. Journal of Chemometrlcs, 13, 295-309.
5. BRO, R., & KIERS, H. A. L. (2003). A new efficient method for determining the number of components in PARAFAC models. Journal of Chemometrics, 17, 274-286. doi:10.1002/cem.801
6. BRO, R., & Smilde, A. K. (2003). Centering and scaling in component analysis. Journal of Chemometrics, 17, 16-33. doi: 10.1002/cem.773
7. CARROLL, J. D., & CHANG, J.-J. (1970). Analysis of individual differences in multidimensional scaling via an n-way generalization of "Eckart-Young" decomposition. Psychometrika, 35, 283-319. doi:10.1007/BF02310791
8. CATTELL, R. B. (1966). The meaning and strategic use of factor analysis. In J. R. Nesseiroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (pp. 174-243). Chicago: Rand McNally.
9. Ceulemans E., & Kiers, H. A. L. (2006). Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method. British Journal of Mathematical & Statistical Psychology, 59, 133-150. doi:10.1348/000711005X648l7
10. CEULEMANS, E., & Kiers, H. A. L. (2009). Discriminating between strong and weak structures in three-mode principal component analysis. British Journal of Mathematical & Statistical Psychology, 62, 601-620.
11. DE JONG, S., & Kiers, H. A. L. (1992). Principal covariates regression: Part I. Theory Chemometria, & Intelligent Laboratory Systems, 14, 155-164.
12. EFRON, B., & Tibshirani, R. J. (1993). An Introduction to the bootstrap. New York: Chapman & Hall/CRC.
13. HARSUMAN, R. A. (1970). Foundations of the PARAPAC procedure: Models and conditions for an explanatory multi-modal factor analysis. Working Papers in Phonetics, 16, University of California Press, 1-84.
14. Harshman, R. A., & De Sarbo W. S. (1984). An application of PARAFAC to a small sample problem, demonstrating preprocessing orthog onality constraints, and split-half diagnostic techniques. In H. G. Lass C. W. Snyder, Jr., J.A. Hattie, & R. P. McDonald (Eds.), Research methods for multimode data analysis (pp. 602-642). New York Praeger.
15. HARSHMAN, R. A., LADEFOGED, P., & GOLDSTEIN, L. (1977). Factor analysis of tongue shapes. Journal of the Acoustical Society of America, 62, 693-707.
16. HARSHMAN, R. A., & LUNDY, M. E. (1984). Data preprocessing and the extended PARAFAC model. In H. G. Law, C. W. Snyder, Jr., J. A. Hattie, & R. P. McDonald (Eds.), Research methods for multbnode data analysis (pp. 216-284). New York: Praeger.
17. HARSHMAN, R. A., & Lunday, M. E. (1994). PARAFAC: Parallel factor analysis. Computational Statistics & Data Analysls, 18, 39-72.
18. HOTELLING, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441.
19. HOTELLING, H. (1936). Relations between two sets of variates. Biometrika, 28, 321-377.
20. Kiers, H. A. L. (2000). Towards a standardized notation and terminology in multiway analysis. Journal of Chemometrics, 14, 105-122.
21. Kiers, H. A. L. (2004). Bootstrap confidence intervals for three-way methods. Journal of Chemometrics, 18, 22-36. doi: 10.1002/cem.841
22. Kiers, H. A. L., & VAN Mechelen, I. (2001). Three-way component analysis: Principles and illustrative application. Psychological Methods, 6, 84-110. doi:10.1037/1082-989X.6.1.84
23. KROONENBERG, P. M. (1983). Three-mode principal component analysis. Theory and applications. Leiden: DSWO Press.
24. KROONENBERG, P. M. (2008). Applied multiway data analysis. Hoboken, NJ: Wiley.
25. KROONENBERG, P. M., & DE Leeuw, J. (1980). Principal component analysis of three-mode data by means of alternating least squaresal algorithms. Psychometrika, 45, 69-97. doi:10.1007/BF02293599
26. Kuppens P., Van Mechelen, I., & RIJMEN, F. (2008). Toward disentangling sources of individual differences in appraisal and anger. Journal of Personality, 76, 969-1000. doi:10.111/j.1467-6494.2008.00511.x
27. Mischel, W., & SHODA, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Reviews in Psychology, 49, 229-258.
28. PEARSON, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559-572.
29. RIU, J., & BRO, R. (2003). Jack-knife technique for outlier detection and estimation of standard errors in PARAFAC models. Chemometrics & Intelligent Laboratory Systems, 65, 35-49. doi:10.1016/S0169-7439(02)00090-4 (Pubitemid 35449777)
30. Sidiropoulos, N. D., & BRO, R. (2000). On the uniqueness of multi-linear decomposition of n-way arrays. Journal of Chemometrlcs, 14, 229-239.
31. SMILDE, A. K., BRO, R., & GELADI, P. (2004). Multi-way analysis with applications in the chemical sciences. Chichester, U.K.: Wiley
32. SMILDE, A. K., & Kiers, H. A. L. (1999). Multi-way covariates regression models. Journal of Chemometrics, 13, 31-48.
33. SMILDE, A. K., Westerhuis, J. A., & BOQUÉ R. (2000). Multiway multiblock component and covariates regression models. Journal of Chemometrics, 14, 301-331.
34. SPIELBERGER, C. D., Crane R. S., KEARNS, W. D., PELLEGRIN, K. L, Rickman R. L., & Johnson, E. H. (1991). Anger and anxiety in es sential hypertension. In C. D. Spielberger I.G. Sarason, Z. Kulcsar, & G. L. Van Heck (Eds.), Stress and emotion: Anxiety, anger; and curiosity (Vol. 14, pp. 265-283). Washington, DC: Hemisphere.
35. SPIELBERGER, C. D., JOHNSON, E. H., Russell, S.F., Crane R. J., Jacobs, G. A., & Wonnaz T. J. (1985). The experience and expression of anger: Construction and validation of an anger expression scale. In M. A. Chesney & R. H. Rosenman (Eds.), Anger and hostility in canliovascular and behavioral disorders (pp. 5-30). Washington, DC: Hemisphere.
36. TEN BERGE, J. M. F. (1993). Least squares optimization in multivarlate analysis. Leiden DSWO Press.
37. TUCKER, L R. (1951). A method for synthesis of factor analysis studies (Personnel Research Section Rep. No. 984). Washington, DC: Department of the Army.
38. Wilderjans T. F., CEULEMANS, E, & VAN MECHELEN, I. (2008). The CHIC model: A global model for coupled binary data. Psychometrika, 73, 729-751. doi:10.1007/S11336-008-9069-9
39. Wilderjans T. F., CEULEMANS, E, & VAN MECHELEN, I. (2009). Simultaneous analysis of coupled data blocks differing in size: A comparison of two weighting schemes. Computational Statistics & Data Analysis, 53, 1086-1098. doi:10.1016/j.csda.2008.09.031
40. WOOD, L. M. (2004). Dimensions of brand purchasing behaviour: Consumers in the 18-24 age group. Journal of Consumer Behaviour, 4, 9-24.